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                        <h2>鲁鹏</h2>
                        <h3>副教授，博士生导师</h3>
                        <p>鲁鹏，男，1978年9月生于湖北省武汉市。2006年7月获得中国科学院自动化研究所博士学位，2008 年到2010 年在北京大学人机交互与多媒体实验室从事博士后研究，2010年至今受聘于北京邮电大学计算机学院。</p>
                        <p>主持纵向项目包括：国家自然科学基金项目"面向概念设计的虚实融合环境交互技术研究"，博士后基金项目"自然的三维概念草图绘制技术研究"；作为负责人完成国家863项目"基于双目立体视觉的自然交互技术"的研究工作；同时，主持多项横向课题的研究工作。</p>
                        <p>指导本科生获得2011年“全国大学生智能设计竞赛”一等奖; 2013年获得“北京邮电大学第十一届教学观摩评比”二等奖。 指导本科生获得2016年“全国大学生智能设计竞赛”二等奖。</p>
                        <p class="about-list-outcome"><b>研究方向：</b>机器学习、计算机视觉、人机交互</p>
                        <p class="about-list-outcome"><b>承担课程：</b>机器视觉、多模态信息处理、智能机器人</p>
                        <p class="about-list-outcome"><b>工作地点：</b>北京邮电大学新科研楼812房间</p>
                        <p class="about-list-outcome"><b>电子邮箱：</b>lupeng@bupt.edu.cn</p>
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                <p>带你走进计算机视觉与深度学习的大门</p>
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                        <h4 class="title" ><a href="../../pages/courses/cv_3d.html">计算机视觉之三维重建篇-精简版</a></h4>
                        <p class="description" >什么是摄像机？它的成像原理是什么？单张图像可以重建场景吗？什么是多视图几何？它有什么性质？如何通过图像重建3d场景？这门课会代领同学们进入视觉重建技术的世界，不再囿于"一张图像"的 平面视角。<br/><br/><br/><br/></p>
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                        <h4 class="title" ><a href="../../pages/courses/cv_basic.html">计算机视觉基础</a></h4>
                        <p class="description" >本课程将着眼于计算机视觉的基本框架，带领大家从最基础的必备图像处理技巧开始， 首先探索图片基本信息（诸如边缘、尺度不变的特征点，直线或基本图形的拟合、纹理等）的提取和应用。 然后,我们将一起着眼于计算机视觉的基本任务的解决方法，即分割问题、识别问题、检测问题。 同时，本课程也会带领大家进入立体视觉的世界，以运动恢复结构为例打开3D大门。</p>
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                        <h4 class="title" ><a href="../../pages/courses/cv_dl.html">计算机视觉与深度学习</a></h4>
                        <p class="description" >什么是深度学习？什么是神经网络？神经网络都有哪些结构？能够完成计算机视觉中的什么任务？ 本课程将从基本的线性分类器、全连接神经网络、卷积神经网络开始，步入深度学习的世界。 探究如何利用他们解决分类、分割、检测问题。 同时，也将讲到网络的可视化的方法。除了常用的判别模型，我们也会讲到生成模型， 比如VAE、GAN等的模型结构、论文解读和应用。</p>
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                <ul type="disc">
                    <li>Yin W, <b>Lu P</b>, Zhao Z, et al. “Yes, Attention Is All You Need for Exemplar based Colorization", Proceedings of the 29th ACM International Conference on Multimedia (MM '21), Virtual Event, China., 2021[C]. ACM, New York, NY, USA, October 20–24, 2021.</li>
                    <li><b>Lu P</b>, Yu J, Peng X, et al. Gray2ColorNet: Transfer More Colors from Reference Image[C]// 28th ACM International Conference on Multimedia. ACM, 2020.</li>
                    <li><b>Lu P</b>, Zhang H , Peng X , et al. Learning the Relation between Interested Objects and Aesthetic Region for Image Cropping[J]. IEEE Transactions on Multimedia, 2020, PP(99).</li>
                    <li><b>Lu P</b>, Liu J, Peng X, et al. Weakly Supervised Real-time Image Cropping based on Aesthetic Distributions[C]// 28th ACM International Conference on Multimedia. ACM, 2020.<b>(Oral)</b></li>
                    <li><b>Lu P</b>, Zhang H , Peng X , et al. Aesthetic guided deep regression network for image cropping[J]. Signal Processing: Image Communication, 2019, 77:1-10.</li>
                    <li><b>Lu P</b>, Yu J , Peng X . Deep Conditional Color Harmony Model for Image Aesthetic Assessment[C]// 24th International Conference on Pattern Recognition. 2018.</li>
                    <li><b>Lu P</b>, Peng X, Yu J, et al. Gated CNN for visual quality assessment based on color perception[J]. Signal Processing: Image Communication, 2019, 72(C):105-112.</li>
                    <li><b>Lu P</b>, Yuan C, et al. Image color harmony modeling through neighbored co-occurrence colors[J]. Neurocomputing, 2016.</li>
                    <li><b>Lu P</b>, Peng X, et al. Towards aesthetics of image: A Bayesian framework for color harmony modeling[J]. Signal Processing Image Communication, 2015.</li>
                    <li><b>Lu P</b>, Peng X, Zhu X, et al. An EL-LDA based general color harmony model for photo aesthetics assessment[J]. Signal Processing, 2014,120.</li>
                    <li><b>Lu P</b>, Kuang Z , Peng X , et al. Discovering Harmony: A Hierarchical Colour Harmony Model for Aesthetics Assessment[C]// Asian Conference on Computer Vision. Springer, Cham, 2014.</li>
                    <li><b>Lu P</b>, Peng X, Zhu X, et al. Finding More Relevance: Propagating Similarity on Markov Random Field for Image Retrieval[J]. Signal Processing: Image Communication, 2013,32.</li>
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